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Balios - The Engine for Bayesian Logic Programs

Kristian Kersting; Uwe Dick
In: Jean-François Boulicaut; Floriana Esposito; Fosca Giannotti; Dino Pedreschi (Hrsg.). Knowledge Discovery in Databases: PKDD 2004, 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings. European Conference on Knowledge Discovery in Databases (PKDD-2004), September 20-24, Pisa, Italy, Pages 549-551, Lecture Notes in Computer Science (LNCS), Vol. 3202, Springer, 2004.


Inductive Logic Programming (ILP) [4] combines techniques from machine learning with the representation of logic programming. It aims at inducing logical clauses, i.e, general rules from specific observations and background knowledge. Because of focusing on logical clauses, traditional ILP systems do not model uncertainty explicitly. On the other hand, state-of-the-art probabilistic models such as Bayesian networks (BN) [5], hidden Markov models, and stochastic context-free grammars have a rigid structure and therefore have problems representing a variable number of objects and relations among these objects. Recently, various relational extensions of traditional probabilistic models have been proposed, see [1] for an overview. The newly emerging field of (SRL) studies learning such rich probabilistic models.

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